Study on the Overfitting of the Artificial Neural Network Forecasting Model

Because of overfitting and the improvement of generalization capability (GC) available in the construction of forecasting models using artificial neural network (ANN), a new method is proposed for model establishment by means of making a low-dimension ANN learning matrix through principal component analysis (PCA). The results show that the PCA is able to construct an ANN model without the need of finding an optimal structure with the appropriate number of hidden-layer nodes, thus avoids overfitting by condensing forecasting information, reducing dimension and removing noise, and GC is greatly raised compared to the traditional ANN and stepwise regression techniques for model establishment.     

Classification of mineral deposits into types using mineralogy with a probabilistic neural network

In order to determine whether it is desirable to quantify mineral-deposit models further, a test of the ability of a probabilistic neural network to classify deposits into types based on mineralogy was conducted. Presence or absence of ore and alteration mineralogy in well-typed deposits were used to train the network. To reduce the number of minerals considered, the analyzed data were restricted to minerals present in at least 20% of at least one deposit type. An advantage of this restriction is that single or rare occurrences of minerals did not dominate the results. Probabilistic neural networks can provide mathematically sound confidence measures based on Bayes theorem and are relatively insensitive to outliers. Founded on Parzen density estimation, they require no assumptions about distributions of random variables used for classification, even handling multimodal distributions. They train quickly and work as well as, or better than, multiple-layer feedforward networks. Tests were performed with a probabilistic neural network employing a Gaussian kernel and separate sigma weights for each class and each variable. The training set was reduced to the presence or absence of 58 reported minerals in eight deposit types. The training set included: 49 Cyprus massive sulfide deposits; 200 kuroko massive sulfide deposits; 59 Comstock epithermal vein gold districts; 17 quartzalunite epithermal gold deposits; 25 Creede epithermal gold deposits; 28 sedimentary-exhalative zinc-lead deposits; 28 Sado epithermal vein gold deposits; and 100 porphyry copper deposits. The most common training problem was the error of classifying about 27% of Cyprus-type deposits in the training set as kuroko. In independent tests with deposits not used in the training set, 88% of 224 kuroko massive sulfide deposits were classed correctly, 92% of 25 porphyry copper deposits, 78% of 9 Comstock epithermal gold-silver districts, and 83% of six quartzalunite epithermal gold deposits were classed correctly. Across all deposit types, 88% of deposits in the validation dataset were correctly classed. Misclassifications were most common if a deposit was characterized by only a few minerals, e.g., pyrite, chalcopyrite,and sphalerite. The success rate jumped to 98% correctly classed deposits when just two rock types were added. Such a high success rate of the probabilistic neural network suggests that not only should this preliminary test be expanded to include other deposit types, but that other deposit features should be added     

煤矿立井井筒非采动破裂的人工神经网络预测

应用人工神经网络的基本原理,建立了一个基于神经网络的煤矿立井井筒非采动破裂的预测系统,实现了立井井筒破裂预测的智能化。最后将神经网络预测结果与数值计算结果对比,认为应用人工神经网络对立井井筒破裂时间的预测比较准确、实用。     

基于互联网数据的城市社区租金评估及 空间格局制图——以深圳市为例

准确刻画精细化尺度下的城市房租空间格局,对于研究城市居住行为、提高城市规划合理性十分重要。文章提出了一种基于互联网房租数据作为可靠数据源的城市房租空间格局制图方法。以深圳市作为研究区,通过广泛采集开放平台中用户发布的租房信息,绘制社区行政区尺度的房租空间分布图。房租空间制图涉及到对于没有样本数据区域平均房租的评估,因此,选取一系列与房租相关的房产属性、房屋区位及配套设施的评价指标,采用前馈神经网络技术构建评估模型。以2015年深圳市的住宅租赁市场作为研究对象,通过对结果的分析,以及与权威部门发布的统计数据进行比较,表明此方法能够有效地绘制社区尺度下城市房租的空间分布,模型预测结果的误差（%RMSE）为13.87%。所使用的互联网房租数据、POIs数据及前馈神经网络的建模工具均是开源的,而且所提出的方法论具有普适性,能够应用于其他研究区的房租空间格局制图,具有实践意义。     

矿井煤层底板突水预测新方法研究

本文针对煤矿矿井煤层底板突水系统为一非线性系统的特性,提出采用对非线性问题具有良好适用性的人工神经网络系统（以下简称神经网络）,进行煤层底板突水预测。以作者们研制,使用神经网络的实践为基础,阐述系统、建模方法、适用条件和应用问题,并在焦作矿务局演马庄矿、焦作金科尔集团方庄煤矿对所建立的煤层底板突水预测神经网络进行生产性检验,取得良好的结果,说明该系统应用于煤层底板突水预测的可靠性。     

基于人工神经网络的黄河下游枯季径流预测研究

通过分析黄河下游枯季径流的影响因素 ,主要为花园口水文站径流量和下游的引黄量这两个因子 ,花园口水文站径流量和下游的引黄量可作为输入层中的影响因子 ,下游利津站的流量作为输出层。应用多层前向人工神经网络理论 ,构造四套枯季径流实时预测的BP神经网络模型 ,使用花园口—利津水文站 2 6年的完整序列测流资料训练和检验网络并用于预测     

Wind speed prediction using statistical regression and neural network

Prediction of wind speed in the atmospheric boundary layer is important for wind energy assessment, satellite launching and aviation, etc. There are a few techniques available for wind speed prediction, which require a minimum number of input parameters. Four different statistical techniques, viz., curve fitting, Auto Regressive Integrated Moving Average Model (ARIMA), extrapolation with periodic function and Artificial Neural Networks (ANN) are employed to predict wind speed. These methods require wind speeds of previous hours as input. It has been found that wind speed can be predicted with a reasonable degree of accuracy using two methods, viz., extrapolation using periodic curve fitting and ANN and the other two methods are not very useful.     

基于主成分的时间域航空电磁数据神经网络反演仿真研究(英文)

传统上,时间域航空电磁数据通过拟合迭代反演计算得到大地模型,然而,由于航空电磁数据道间的较强相关性,导致病态反演,并引起超定问题;同时电磁数据的相关性使其与模型参数的映射关系复杂,增加了反演的复杂度。采用主成分分析法将航空电磁数据变换为正交的较少数量的主成分,不仅降低了数据道间的相关性,减小了数据量,同时压制了数据的不相关噪声。本文利用人工神经网络(ANN)逼近主成分与大地模型参数间的映射关系,避免了传统反演算法中雅克比矩阵的复杂计算。层状模型的主成分神经网络与数据神经网络的反演结果对比显示,主成分神经网络反演方法网络结构简单,训练步数少,反演结果好,特别是对于含噪数据。准二维模型的主成分ANN、数据ANN以及Zhody方法的反演结果显示了主成分神经网络具有更接近真实模型的反演效果,进一步证明了主成分神经网络反演方法适合海量航空电磁探测数据反演。     

基于BP神经网络的农户生计风险评价模型

当今,农户生计风险在农村社会建设的过程中是最为重要和核心的问题之一。作为基本的社会经济单元,农户正面临着来自经济、社会和自然等各方面的风险。石羊河流域农户由于面临着严峻的风险冲击使其生计脆弱性进一步加剧。所以,急需辨明流域农户面临的主要风险类型,并依此找寻提高农户风险应对能力的对策措施。针对流域农户生计风险的非线性关系和复杂性特征,以及BP神经网络的适用性范围,建立了基于BP神经网络的农户生计风险评价指标体系。并基于入户调查数据,运用熵权法计算得出流域农户生计风险评价指标体系中各种生计风险变量。同时,根据石羊河流域农户所面临生计风险的特征,基于BP神经网络结构模型,确定了其评价结果的表征方法。最终对模型的可行性进行了验证。研究表明：基于BP神经网络的生计风险评价模型对定量化测度农户生计风险的适用性较强,这可为决策部门定量评估农户生计风险提供依据。     

基于神经网络方法的极化雷达地表参数反演

人工神经网络（Artificial Neural Network)是一个由独立处理单元以一定拓扑结构高度连接而成的并行分布式信息处理结构，适于解决各种非线性问题，积分方程（Integrated Equation Model)单散射模型可模拟各种地表参数条件下裸露地表后向散射系数，以IEM为基础生成训练数据，用L波段的C波段SIR-CHH，VV极化单散射后向散射系数数据为神经网络输入，通过后向反馈（BP）神经网络模型可同时反演得到裸露地表条件下地表介电常数，地表相关长度和均方根高度等地表参数。